Blessings of Multiple Good Arms in Multi-Objective Linear Bandits

2026-02-13

Technology Category

• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The multi objective bandit setting has traditionally been regarded as more complex than the single objective case, as multiple objectives must be optimized simultaneously. In contrast to this prevailing view, we demonstrate that when multiple good arms exist for multiple objectives, they can induce a surprising benefit, implicit exploration. Under this condition, we show that simple algorithms that greedily select actions in most rounds can nonetheless achieve strong performance, both theoretically and empirically. To our knowledge, this is the first study to introduce implicit exploration in both multi objective and parametric bandit settings without any distributional assumptions on the contexts. We further introduce a framework for effective Pareto fairness, which provides a principled approach to rigorously analyzing fairness of multi objective bandit algorithms.

Problem

• multi-objective bandits
• implicit exploration
• Pareto fairness
• linear bandits
• good arms

Innovation

• implicit exploration
• multi-objective bandits
• Pareto fairness
• parametric bandits
• greedy algorithms